activity
20202024
most citedScale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

8 citations · 19 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2024

Multi-Scale Representation Learning for Image Restoration with State-Space Model

Yuhong He, Long Peng, Qiaosi Yi +2

Image restoration endeavors to reconstruct a high-quality, detail-rich image from a degraded counterpart, which is a pivotal process in photography and various computer vision syst…

cs.CV2023

Textual Prompt Guided Image Restoration

Qiuhai Yan, Aiwen Jiang, Kang Chen +3

Image restoration has always been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, "all-in-one…

cs.CV2022

Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation

Juncheng Li, Hanhui Yang, Qiaosi Yi +4

Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of paramete…

cs.CV20216 cited

Structure-Preserving Deraining with Residue Channel Prior Guidance

Qiaosi Yi, Juncheng Li, Qinyan Dai +3

Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognitio…

cs.CV20213 cited

Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation

Qinyan Dai, Juncheng Li, Qiaosi Yi +2

Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effec…

cs.CV20211 cited

Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing

Qiaosi Yi, Juncheng Li, Faming Fang +2

Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great suc…